Summary
Yuelyu J is an applied scientist and Information Science PhD candidate at the University of Pittsburgh with eight years of experience building and evaluating retrieval-augmented generation (RAG), multimodal reasoning, and trustworthy AI systems. Her work—published at SIGIR, ACL, and npj Digital Medicine—focuses on reasoning-based knowledge distillation, fairness-aware training, and bias mitigation for clinical AI, and she has translated that research into production experiments at NetEase and AWS to improve user satisfaction through RL-enhanced text generation. At NetEase she helped create the COCO-Music dataset and a Continuous Parameterization approach that balances factuality and emotional style using PPO, a less obvious blend of music-domain data creation and reinforcement learning. Now at Microsoft, she continues to bridge rigorous academic methods with applied engineering to build reliable, equitable multimodal AI, and she is actively seeking collaborations in RAG, multimodal systems, and responsible AI.
8 years of coding experience
2 years of employment as a software developer
南京农业大学
Doctor of Philosophy - PhD, Information Science, Doctor of Philosophy - PhD, Information Science at University of Pittsburgh